# weights['out'] = tf.get_variable('out',shape=[dimension,2], initializer=tf.contrib.layers.xavier_initializer())
# biases['out'] = tf.Variable(tf.random_normal([2]))
return weights, biases
weights, biases = loadWeightsAndBiases()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
bS = 450
vBS = 1
print("before data loads")
dataObject = dh.COCOData(batchsize=int(bS / 2), vbatchsize=vBS)
dataObject.dataLoadByName(objectIDNames[0])
dataObject.loadPureDataByIdName(objectIDNames[0])
pureD, pureL = dataObject.pureData[0], np.asarray(
[[0, 1] for i in range(len(dataObject.pureData[0]))])
anythingElseD, anythingElseL = dataObject.dataLoadAnythingElse()
print("after data loads")
dataPointP = getDataPoint(pureD[0:int(bS / 2)],
weights,
biases,
int(bS / 2),
answers=pureL[0:int(bS / 2)])
dataPointAE = getDataPoint(anythingElseD[0:int(bS / 2)],
